Difference between revisions of "10-601 Ensembles 1"
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You should know how to implement these ensemble methods, and what their relative advantages and disadvantages are: | You should know how to implement these ensemble methods, and what their relative advantages and disadvantages are: | ||
| − | * Bagging | + | * (Ziv - not sure if can do in one lecture if we do boosting) Bagging |
* Boosting | * Boosting | ||
* Stacking | * Stacking | ||
* Multilevel Stacking | * Multilevel Stacking | ||
| − | * The "bucket of models" classifier | + | * (Ziv - not sure if I will do this) The "bucket of models" classifier |
| + | * Random forest | ||
Revision as of 08:55, 12 August 2014
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- Ensemble Methods in Machine Learning, Tom Dietterich
- A Short Introduction to Boosting, Yoav Freund and Robert Schapire.
Summary
You should know how to implement these ensemble methods, and what their relative advantages and disadvantages are:
- (Ziv - not sure if can do in one lecture if we do boosting) Bagging
- Boosting
- Stacking
- Multilevel Stacking
- (Ziv - not sure if I will do this) The "bucket of models" classifier
- Random forest
